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International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.

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International Journal of Engineering Research and DevelopmentISSN: 2278-067X, Volume 1, Issue 1 (May 2012), PP 09-15www.ijerd.com Appraising the Significance of Self Regulated Learning in Higher Education Using Neural Networks S.Anupama Kumar1, Dr.Vijayalakshmi .M.N2 1 Research Scholar, Prist University, Assistant Professor, Department of M.C.A., R.V.C.E, Bangalore, India 2 Associate Professor, Department of M.C.A, R.V.C.E, Bangalore, IndiaAbstract––Students in higher education would aim to achieve good academic performance and good placement beforethey complete their studies. Academic performance of the students can be achieved by providing them good educationalenvironment. To get a good placement a student needs not only good academic performance but also good attitudetowards learning, attitude to adapt to new situation, self efficacy, commitment towards working etc. Educational datamining is a novel technology which can be used to predict the academic performance of the students and help them toachieve their goal. Many classification techniques like decision trees, Bayesian networks, rule based algorithms can beused to predict the student’s academic performance. This paper aims to apply neural networks to predict whether thestudent will achieve his goal using his academic performance and attitude towards self regulated learning. They are alsoused to analyze the importance of self regulated learning in helping the students to achieve their goal. Two algorithmsMultilayer perceptron and SMO are used to analyze the behavior of the student towards achieving his goal. Both thealgorithms were compared using the number of instances predicted correctly, the accuracy and error rate. The output isalso compared with the original data obtained from the institution. It is proved that SMO performs better than theMultilayer perceptron algorithm. After the analysis of the results, it is observed that self regulated learning plays a vitalrole in a student’s life in achieving his goalKey Words and Phrases––Self regulated learning, academic performance, Placement, Multilayer perceptron, SMO. 1. INTRODUCTION Higher Education in India has evolved in distinct and divergent streams to help the students to be employed either inIndia or abroad. The students joining a higher education course are those who already possess a bachelor’s degree andwanted to pursue a Master’s degree. These students generally aim at a good placement before they complete the course.To achieve their goal, students should posses good academic record, self confidence, an attitude towards learning newthings, adapt themselves to any situation, good behavior etc. Educational institutions provide students with good learningenvironment and help them to achieve their academic goals. In [25] Cristobal Romero and et al have explained how thedata mining techniques can be applied on different educational domains. He has also explained how artificial intelligencecan be used to predict student grades using Moodles environment. Data mining techniques can be applied on student database to identify those students who are self learners by nature and help the tutor to motivate them to reach their goalefficiently. This academic performance of the students can be controlled and measured using various techniques. Apartfrom teaching, the educational institutions should provide opportunity to the students to develop self learning ability. Thiscan be achieved by providing them some exercises which can help them to learn and improve their self learningcapability and build their confidence to reach their goal. The students with good academic results are expected to getplaced easily where other students face difficulties in reaching their goal. But there are some situations where somestudent’s with good academic results also struggle to achieve their goal. This paper aims to focus on the importance ofthat area where the attitude or the behavior of the students plays a major role along with the academic performance of thestudent to achieve their goal. Students who are metacognitively, motivationally and behaviorally active participants in their own learning processcan be described as self regulated students (Zimmerman, 1986, 1989). Such students personally initiate and direct theirown efforts to acquire knowledge and skill rather than relying on teachers, parents, or others for instruction. Studentswho are qualified as self regulated students must involve the use of specified strategies to achieve their goals on the basisof self efficacy perceptions. From [4, 5] the following elements are identified as a part of self regulated learning 1.1 Self regulated learning strategies can be defined as actions and processes directed at acquiring information or skill that involve agency, purpose, and instrumentality perceptions by learners. They include such methods as organizing and transforming information, self-consequating, seeking information, and rehearsing or using memory aids (Zimmerman & Martinez-Pons, 1986). 1.2 Self Efficacy is referred as perceptions about ones capabilities to organize and implement actions necessary to attain designated performance of skill for specific tasks. Academic goals such as grades, social esteem, or postgraduation employment opportunities can vary extensively in nature and in time of attainment. 1.3 Commitment to academic Goals: Zimmerman explains how learners represent contemporary actions and conditions in terms of strategies for reaching subsequent goals. He assumes a motivational orientation by learners that is sustained by continuing self-perceptions of efficacy when performing a specific task. Thus in order for students’ strategic actions to be described as self-regulated, one must know their academic goals and perceptions of efficacy. 9

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Appraising the Significance of Self Regulated Learning in Higher Education Using Neural NetworksThis paper aims to appraise the importance of self regulated learning among the students and analyse how self regulatedstudents perform better than others in achieving their goal. This paper is divided into three sections namely (2) Background investigation and related work (3) Application of Neural network and (4) Conclusion. 2. BACKGROUND INVESTIGATION AND RELATED WORK Education system in India is more centered through the teacher-student relationship. The interaction between theteacher and the student is high even in higher education system where many of the foreign countries have adopted virtuallearning system. Due to this type of learning environment it becomes easy for any tutor to interact with the studentspersonally and bring out the talents present in them. In [9] the author Dominik has explained about the project DISUM used in developing and investigatingcorresponding instructional conceptions, based on an intensive analysis of modelling tasks and of students’ learningprocesses in Germany. Jong-Ki Lee [19] has explained the effects of self regulated learning in an e-learning system by adeveloping a model and proved the model to be satisfactory using empirical methods .In [11] Retta Sweat Guy and et alhas explained the importance of self efficacy beliefs and demonstrated it for computer skills. The research has been doneby measuring ability of the student’s computer skills using questionnaires and simple correlation techniques. In [13] theauthor has explained a study conducted in a 5th grade science classroom, and presented a model derived from thestudents’ activity sequences. He also interpreted the model structure as aggregate patterns of their learning behaviors, andlinks these patterns to students’ use of self-regulated learning strategies. The results illustrates that those who teach anagent demonstrate better learning performance and better use of metacognitive monitoring behaviors than students whoonly learn for themselves. Mark McMahon & Joe Luca in [15] have described a model in assessing students’ self-regulatory skills by developing an online conceptual frame work. Teresa Hurley[26] has explained how data miningtechniques can be used effectively to motivate the students in an online environment. Students who join higher education are more interested in getting a placement before they complete the course. Toget a good placement, possessing consistently good academic record is very important. Therefore it becomes importantfor the tutors to monitor and control the academic performance of the students. This can be achieved by predicting theperformance of the students before they attend the university examination and help them if they tend to get a lowerpercentage. In [23, 24], the author has explained the importance of prediction of students percentage and the role of datamining in doing the same. Students who are identified as good performers are definitely self regulated learners. They know how to managetheir time and plan their studies accordingly. To achieve their goal they should also possess good communication skill,self confidence, attitude towards learning etc. This paper aims at measuring the importance of these skills along with theacademic performance in helping the students to achieve their goal. Neural networks are applied to analyse the behaviour the students and predict whether the students are able toachieve their goal in this paper. Two algorithms like Multilayer perceptron and SMO are applied to analyse the student’srecords. 3. APPLICATION OF NEURAL NETWORKS Neural Networks is a field of Artificial Intelligence (AI) where, by inspiration from the human brain, find datastructures and algorithms for learning and classification of data. Using this technique a neural network algorithm canlearn by examples, and create an internal structure of rules to classify different inputs and produce one target output.Neural Networks are successfully being used in many areas often in connection with the use of their AI techniques. Theycan also be used a predictive analytics tool for classification problems. In [22] the author has explained how neuralnetworks can be used to predict the student’s performance in higher language in association with clustering. The varioustypes of neural networks include Probabilistic Neural Networks, General Regression Neural Networks, Radial BasisFunction Networks, Cascade Correlation, Functional Link Networks, Kohonen networks, Gram-Charlier networks,Learning Vector Quantization, Hebb networks, Adaline networks, Heteroassociative networks, Recurrent Networks andHybrid Networks. In this paper Multilayer perceptron algorithm and SMO are used to assess the importance of selfregulated learning among students to reach their goal.The following procedures are encountered while implementing the neural network algorithms.3.1 Selection of Data Set Student’s joining a Master’s degree programme would be aiming to get a placement before the end of the course. Toget a placement, students with consistently good academic record and a higher attitude towards learning would be thechoice for any organization. Therefore it is important for a student to hold a good academic record and has an attitudetowards learning. Students with high percentage of marks would be definitely self regulated learners and good planners.To appraise the behavior of a student towards learning, data pertaining to their self regulated learning activities should betaken. A student data base for a group of 112 students has been taken from a master’s degree programme. The attributescomprises of the academic performance of the students in the examination and data pertaining to self regulated academicactivities conducted for the student. The academic performance of the student comprises of the percentage of marks 10

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Appraising the Significance of Self Regulated Learning in Higher Education Using Neural Networksobtained by the student from his 10th standard to under graduation as well the percentage of marks of the student in thefirst IV semesters of the master’s course as it is considered for checking the consistency in the academic performance ofthe student. The students are recruited to do a project for a period of six months in an industry. This project would bejointly guided by the faculty members of the department where they are studying and by project head of the organizationwhere they the project is assigned to them. The project is also jointly evaluated by both the internal and external guide ofthe student. Therefore the student has to co-ordinate with both the guides to complete the project and submit it in the endof the course.The table 1 describes the data set used in this research. Table 1: Data Set of Students S. No Attribute Information Category 1 UID University id Numeric 2 X percentage %of marks obtained Numeric 3 XII percentage %of marks obtained Numeric 4 Degree percentage %of marks obtained Numeric 5 I sem Percentage %of marks obtained Numeric 6 II sem Percentage %of marks obtained Numeric 7 III sem percentage %of marks obtained Numeric 8 IV sem percentage %of marks obtained Numeric 9 Overall aggregate %of marks obtained Numeric 10 No. of running backs No. of subjects where the student has Numeric a back up 11 Phase 1 Submission of synopsis in time Nominal 12 Phase 2 Submission of SRS in time Nominal 13 Phase 3 Presentation on SRS Nominal 14 Phase 4 Submission of design Nominal 15 Phase 5 Presentation on design Nominal 16 Phase 6 Submission of report and final Nominal presentation 17 Predicted Prediction by the miner depending Nominal achievement upon the academic and project data The first attribute UID is the university seat No of the student which is ignored while calculating the target variable.The attributes 1-10 defines the academic performance of the students and the attributes from 11-16 explains the datarelated to the self regulated learning activities .The attributes 11-16 are nominal attributes where the student will beawarded with a grade (G/N) in each phase jointly by both the guides depending on his performance and attitude towardsdoing the work where “G” indicates the performance of the student as “Good and Satisfactory ” and “N” indicates the“Student needs to Improve”. The 17th attribute is the target variable which is predicted by the miner upon which thealgorithms are implemented. This target variable is set as (YES/NO) stating whether the student has achieved his goal ornot.3.2 Implementation of Multilayer Perceptron: Multilayer perceptron algorithm is one of the widely used algorithms in neural network. The algorithm is developedusing three layer architecture. They are the input layers, the hidden layers and the output layer. The input layer consists ofthe input attributes and the output layer consists of the target attribute. 11

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Appraising the Significance of Self Regulated Learning in Higher Education Using Neural Networks Fig 1: Evaluation of Multilayer Perceptron The above Fig 1 explains the working of the Multi layer perceptron algorithm. The input layers are fed to the systemand connected to the hidden layer and the hidden layer is connected to the output layer. The input layers are representedin green color and the hidden layers are formed using the red color. The output nodes are represented using the points inthe orange color and the target attributes are set in the square boxes. The lines between the nodes explain the flow of theinput nodes towards the target variable with the help of hidden layers. Each hidden layer is connected to the previous andnext layer nodes and the edges are associated with weights. The number of hidden layers to be assigned is calculatedusing (no. of attributes + classes)/2. The learning algorithm is an essential part of the neural network. The learningalgorithm rate can be increased or decreased depending on the need of the system. The learning rate is set to 0.3 toachieve better results. At each node and layer weights are assigned to each attribute. The entire hidden layer will assignweight to the attributes. These values depend on the learning rate given to the algorithm. Table 2 explains theassignement of weights to each attribute at node 2. Table 2: Weights assigned to Attributes at Node2 S. No Attribute Weight 2 X percentage -0.18 3 XII percentage -0.39 4 Degree percentage -0.92 5 I sem Percentage -1.48 6 II sem Percentage -0.26 7 III sem percentage -0.75 8 IV sem percentage -0.83 9 Overall aggregate -0.39 10 No. of running backs 2.01 11 Phase 1 1.40 12 Phase 2 0.61 13 Phase 3 0.3 14 Phase 4 -0.21 15 Phase 5 -0.6 16 Phase 6 1.81 12

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Appraising the Significance of Self Regulated Learning in Higher Education Using Neural Networks3.3 Implementation of SMO: SMO is one of the simplest algorithms implemented with support vector machines. The algorithm is built usingstatistical methods and uses logistic regresstion to predict the target variable. A linear kernel model is implemented toclassify the classifiers YES/NO. A threshold value is calculated at every step for optimizing the output. The followingtable 3 gives the threshold value and the weights assigned for each attribute. Table 3: Weights assigned by SMO S. No Attribute Weight 2 X percentage -0.2059 3 XII percentage 0.5073 4 Degree percentage -0.407 5 I sem Percentage -1.0627 6 II sem Percentage -0.784 7 III sem percentage -0.038 8 IV sem percentage -0.7512 9 Overall aggregate -0.2544 10 No. of running backs 1.5953 11 Phase 1 1.3368 12 Phase 2 0.9876 13 Phase 3 0.8344 14 Phase 4 0.5366 15 Phase 5 -0.0678 16 Phase 6 1.09163.4 Comparison of Multilayer Perceptron and SMO: When both the algorithms are executed for the given data set, the following points are observed 1. Both the algorithms classified 108 instances correctly 2. They classified 4 instances incorrectly. Table 4: Confusion Matrix obtained by Multilayer perceptron and SMO Total No of Instances:112 No of instances classified No of instances classified correctly incorrectly Classified as 64 2 YES(Achieving goal ) Classified as NO (Not 2 44 achieving the goal) 3. The accuracy of the algorithms can be measured using the classification accuracy. It is defined using the proportion of correctly classified rows in a set r. The classification error rates can be verified using the true positive and false negative rates of the algorithm. From the table 5 it is clear that both the algorithms possess equal TP and FP rate. The Recall and F-Measure values are also same for both the algorithms. ROC area measures discrimination, that is, the ability of the test to correctly classify the instances whether they achieve the goal or not. SMO’s ROC area is less than the Multilayer perceptron which reveals that the algorithm is better than the multilayer perceptron. Table 5: Comparison of Multilayer perceptron and SMO Algorithm TPRate FPRate Precision Recall F- ROC Measure area Multilayer 0.964 0.038 0.964 0.964 0.964 0.972 perceptron SMO 0.964 0.038 0.964 0.964 0.964 0.963 4. The confusion matrix obtained reveals that 64 students are able to achieve their goal while 44 students struggle to achieve their goal. This is compared against the original data received from the educational institution. The original data reveals that 66 students have achieved their goal while 46 students could not achieve the goal. From the data it is clear that the algorithm is 97.9% accurate for the given data set. 13